Trade Liberalization, Internal Migration and Regional Income Differences: Evidence from China
Bibliographic record
Abstract
International trade and the internal movement of goods and people are closely related. China ‐ increasingly open and with massive internal migration flows ‐ provides an ideal setting to study these interrelationships. We develop a general equilibrium model of internal and external trade with migration, featuring both trade and migration frictions. Using unique province-level data on internal and external trade, and recent micro-census data on internal migration, we estimate international and internal trade costs and internal migration costs. We find all these costs declined substantially after China joined the WTO. We use the model to quantify and decompose the effects of liberalizing trade (international and internal) and relaxing internal migration restrictions on China’s aggregate welfare, internal migration, and regional income differences. We find tha external trade liberalization has a large impact on China’s trade to GDP ratio, but modestly increases aggregate welfare while increasing regional income differences. In contrast, reducing internal trade costs generates larger welfare gains and reduces regional income differences. While both increase migration flows, migration cost reductions are substantially more important for migration. More surprisingly, lower migration costs only modestly increase aggregate welfare, but substantially decreases regional income differences. Our results suggest that internal market liberalization is much more important than the external trade liberalization as a source of China’s post-WTO improvement in aggregate welfare and reduction in regional income inequality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".